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In developing countries, residual biomass usage by means of anaerobic digestion offers several benefits and opportunities, such as a sustainable energy source, production of organic fertilizers and new agrobusiness models. In Latin America, Colombia is one of the most promising markets for the implementation of this technology in terms of availability of biomass and economic growth, as recently reported by local government organizations. In this paper, special attention is given to Antioquia, a department of Colombia with the largest farms of cattle and pigs, according to information reported in 2018 by the Ministry of Agriculture and Rural Development. It is estimated that manure from the porcine subsector in Antioquia has an approximate technical-energy potential of 1,896 TJ year-1, varying from 1,611 to 2,186 TJ year-1, corresponding to the 95% confidence interval. In the case of manure generated by the livestock subsector in Antioquia, it is estimated a theoretical energy potential of 8,566 TJ year-1. However, traditional extensive production systems disseminate manure through the pastures turning centralization of the available residual biomass a difficult task and not senseful. Based on the local practices of the specialized dairy subsector, it is estimated that manure collected during the milking process could reached up to 25% of the total available. Biochemical conversion of this amount of biomass has an estimated technical-energy potential of 187 TJ year-1, varying from 156 and 236 TJ year-1, corresponding to the 95% confidence. The aim of this article is to estimate the technical-energy potential for the livestock and porcine subsectors in the Department of Antioquia, based on the available residual biomass according to local farming practices. [ABSTRACT FROM AUTHOR]

The objective of this research was to analyze energy security in Ecuador, which is interconnected with water and food security. As a methodology, 21 indicators grouped into the following dimensions were analyzed: availability, infrastructure, energy prices, efficiency, social impact, environment and governance. For each indicator, a cross-sectional analysis was carried out with the countries of Colombia, Peru and Bolivia, along with a longitudinal analysis from 2011 to 2021. The main contribution of this study is the characterization of the Ecuadorian energy system as available, accessible, affordable and renewable in an electrical matrix but inefficient and heavily dependent on fiscal resources. The factors that may affect Ecuador's energy security were determined to be the risk of a reduction in income due to it becoming a net importer of energy, climate change, socio-environmental conflicts, poor diversification, energy subsidies and weak governance. A policy is proposed aimed at decoupling development from fossil fuels, with a systemic vision considering the complexity of interactions with other economic sectors, such as water and food, among others. [ABSTRACT FROM AUTHOR]

Climate change might affect energy production and therefore the energy security of a country or region. This situation may impact renewable energy sources such as hydro power, leading to consequences on energy transition strategies. This might be critical in sensitive regions to climate change, one of them being the Caribe and northern South America. Since there are numerous energy systems based on sensitive technologies worldwide, it is necessary to introduce techniques to analyze the effects of climate change on different possible energy transition paths. The goal of this study is to develop and assess a method to analyze one of the most critical effects faced by climate change for societies worldwide: the sensitivity of the energy systems to climate change. This is especially critical in developing countries, in locations where temperatures will strongly increase in the following years. To assess this effect, this study proposes a vulnerability index (VI) to evaluate the vulnerability of an on-grid electricity system to climate change at the national and regional scales. This index was assessed using a Monte-Carlo method for uncertainty. The case of Colombia, a country with a system based on hydropower (> 70%) is used to illustrate the method. VI is based on variables related to climate change, the energy matrix, and vulnerability. Results show that the regions with the larger vulnerability correspond to the more energy-demanding ones. The VI for these regions is greater than 50% of the maximum possible vulnerability; meanwhile, the vulnerability of the whole country was estimated as 43%. [ABSTRACT FROM AUTHOR]

Increasing awareness of climate change has induced demand for action most notably. As public demand for action on climate change increases, conversion to energy sources with lower greenhouse gas (GHG) intensity will accelerate. Experience during the COVID-19 pandemic provided insight into how atmospheric conditions will respond to lower GHG emissions. A low-carbon future will require decarbonization of the energy supply mix for electrical production and industrial processes. Coal demand likely will decrease more rapidly than other fossil energy sources, replaced by natural gas and renewable energy sources that have lower GHG intensity and that will be available readily and economically. This decline will accelerate as China focuses on its carbon neutrality goals, the U.S. re-engages in the Paris Agreement, and India moves to a lower carbon future. However, perturbations in the decline will inevitably occur in response to global issues (e.g., pandemic, military conflict). Carbon capture, utilization, and storage (CCUS) technologies can reduce GHG emissions from coal as an energy source, but the capital and operation costs of CCUS remain high, which translates to slow commercial deployment. In this study, a literature review and interviews with industry experts and business leaders were conducted to understand the current and projected role of fossil fuels, primarily coal, in the global energy matrix; present their contributions to GHGs; analyze the effects of social expectations and climate policy on energy choices and coal demand; and describe the expected impacts on coal production for decades to come. [ABSTRACT FROM AUTHOR]

• Proposed an intelligent approach for transmission line fault analysis based on capsule network. • An unsupervised model is developed which requires no manual labeling of the data. • The performance against line parameter variation and high impedance faults is assessed. • Noises are considered to justify the robustness of the proposed approach. • The performance is compared with the baseline capsule network and existing approaches. Detection and classification of transmission line (TL) faults are key factors for the fault root cause analysis and rapid restoration of the power network. Deep learning can extract representative features automatically from big data, thus becoming an important breakthrough in fault analysis. Existing deep learning-based fault diagnosis models rely on a large number of data obtained from a variety of fault conditions to be generalized. Indeed, in the TL domain, it is difficult to define and, therefore to collect the fault incepted features of all possible fault scenarios. This paper proposes an unsupervised framework for fault detection and classification (FDC) of TL based on a capsule network (CN). Instead of using the baseline CN, an extension to this with a sparse filtering technique is adopted in this work. The capsule network with sparse filtering (CNSF) voluntarily learns the expensive fault features and significantly improves the model performance without involving a large number of data. The proposed scheme receives 1 / 2 cycle post-fault three-phase signals and encodes them into a single image that is defined as the input for the proposed CNSF model. The effectiveness of the proposed CNSF model is corroborated by four different TL topology confirming the model's adaptability to a topology change in response to intended control action or the switching actions due to cascading faults. Further assessment of the model's performance against noise, high impedance faults (HIF), and line parameter variations are also carried out in order to confirm the high reliability of the proposed model. In addition, a rigorous comparative study is conducted to guarantee the state-of-the-art performance of the proposed model. [ABSTRACT FROM AUTHOR]

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